How Centralization Affects AI Strategy

Want to accelerate your AI strategy by years? Read this. Johnson & Johnson just gave a rare public look at what it takes to move from early experimentation to true enterprise value with Gen AI. (Link in comments) Yogesh Chavda - Thank you for sharing. To their credit, J&J leaned in early, encouraging teams across the company to experiment and engage directly with the technology. They expected that decentralizing innovation would unleash speed and creativity. Instead, it created fragmentation. Hundreds of use cases popped up, but many lacked clear value, measurable outcomes, executive visibility, and connection to business priorities. Now, J&J is moving toward a more centralized model, complete with governance, curated tools, and a cross-functional steering com. This is a familiar pattern. Early experimentation is important, but without a disciplined approach, momentum stalls. Here’s how to avoid that. It starts with identifying the right use cases. Here’s a simple filter I use with my clients: 1. Start with real tasks: What does your team actually do day to day? 2. Pressure test: Is this task repeatable? Business-critical? 3. Prioritize: Focus on high-impact tasks that create friction 4. AI check: Can GenAI make this faster, smarter, or more effective? If the answer’s no, move on. Then conduct disciplined experimenting. The key word here is disciplined. Here is what that means: ✔️ Define success upfront: Set clear outcomes and a baseline so you can measure real impact. ✔️ Secure a senior sponsor: You need someone with authority to unblock, advocate, and decide. ✔️ Launch within 30 days: Urgency sharpens focus. Avoid over-engineering and just start. ✔️ Progress over perfection: An MVP with the right training is more valuable than a flawless concept no one uses. ✔️ Plan for 90 days: Enough time to learn. Short enough to stay agile. J&J learned it the hard way: experimentation without structure doesn’t scale. Disciplined pilots are what move strategy forward. Are you following these practices or losing time you can’t afford to waste? #WomeninAI #AITrainer #FutureofWork #AIinInnovation #AISpeaker #AIAdvisor

Most companies are spending millions on AI tools but missing the one investment that actually drives returns: leadership. 𝗡𝗲𝘄 𝗜𝗕𝗠 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗿𝗲𝘃𝗲𝗮𝗹𝘀 𝗮 𝘀𝘁𝘂𝗻𝗻𝗶𝗻𝗴 𝗴𝗮𝗽: Only 26% of companies have a Chief AI Officer, yet those that do see 10% higher ROI and 24% better innovation performance. Meanwhile, 60% of organizations are running AI pilots that never scale. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴: • AI spend is growing 31% year-over-year • Companies are juggling 11+ AI models (rising to 16+ by 2026) • But without centralized leadership, these investments fragment into low-impact experiments 𝗧𝗵𝗲 𝗖𝗔𝗜𝗢 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝘁𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲. They're the architect who: → Owns enterprise AI strategy (not just tools) → Translates business objectives into technical execution → Aligns the C-suite on governance and ethics → Orchestrates talent, data, and infrastructure at scale 𝗜𝗕𝗠'𝘀 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝘃𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Organizations with centralized AI operating models see 36% higher ROI than decentralized approaches. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗶𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽, 𝗮𝘀𝗸 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳: • Who formally owns AI ROI in your organization? • Are you building AI fluency for strategic conversations? • Do you have the governance structure to scale beyond pilots? AI won't transform your business because you bought the tools. It transforms because you have the right leader building the architecture to scale. 𝗬𝗼𝘂𝗿 𝗺𝗼𝘃𝗲: 1. Map your current AI decision-making structure 2. Stop the pilot paralysis—move toward centralized governance 3. Invest in cross-functional AI leadership development The full IBM report breaks down exactly how top-performing companies are structuring for AI success. 𝗙𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲? 𝗚𝗶𝘃𝗲 𝗶𝘁 𝗮 ♻️ 𝗮𝗻𝗱 𝗳𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿. Want the deep dive? 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗼 𝗺𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 where I unpack the leadership moves that actually drive business results.

As the Tech Leader with leading organizations, I've been grappling with a critical question that many large organizations face: Q: Should we centralize our Data and AI teams or embed them within specific business units? After years of experience and careful analysis, I've come to realize that there's no one-size-fits-all answer. Both approaches have their merits and drawbacks, and the optimal solution often depends on your company's unique culture, goals, and structure. Some things that have worked for me and my team's in the past are - Centralization offers the advantages of standardized practices, economies of scale, and the ability to tackle large, cross-functional projects. However, it can lead to slower response times and a disconnect from business needs. On the flip side, embedding teams within business units ensures closer alignment with stakeholders and faster, more tailored solutions, but it can result in siloed knowledge and duplicated efforts. The key lies in finding the right balance – perhaps a hybrid model that combines the best of both worlds. What's your take on this? Have you found a solution that works particularly well in your organization? #DataStrategy #AIinEnterprise #TechLeadership #AILeaders

Yesterday, an AI leader asked me: “Should we keep our Center of Excellence centralized or start moving data scientists into the business?” It depends. That’s why I’m sharing a few tips to help you answer that strategic question: What’s the size of your company and your AI team? - If your company has 100-500 employees, a centralized CoE will likely be more beneficial. You can set guidelines, standards, technologies, and processes centrally without the overhead of working across business functions (or influencing). What roles do you have on the AI team? - If your team is small (think: “two pizza teams”) and builds AI products data preparation to application development, keep it as one to keep speed, momentum, and your ability to execute. Which business units do you engage with the most?/ Where do you create the most value? - If your highest number of AI projects comes from Procurement and your CoE is no longer able to help other business functions, consider adding headcount to your AI team first. If the projects (and value!) continue to increase, consider moving resources into the business or explore if the Procurement team might need its own satellite AI team (tat stays connected to your CoE). How are you getting ideas and signals from these business units?/ To what extent can tech-savvy multipliers be the connection? - Consider creating a community of multipliers in which stakeholders from the business come together to learn about what AI can do and what tools are available in your company. These multipliers become the go-to person for anything AI in their function and serve as idea scouts that help you vet ideas before they reach your CoE. In yesterday’s conversation, we concluded that keeping AI central and expanding the multiplier community is the best approach for the company at this time. But now I’m curious: How is your AI team organized? PS: If you weren’t at Machine Learning Week this week to talk in person, you’ll find a whole chapter in the AI Leadership Handbook on CoEs and their evolution as well as on how to build a multiplier community. #ArtificialIntelligence #GenerativeAI #IntelligenceBriefing